I've recently joined Amazon Dublin from India and got opportunities to interview with Meta London, Zalando Berlin & some other companies. I extensively researched about companies hiring internationally which support visa & relocation for Tech roles. So sharing list of companies:
Do consider to STAR, if it helped you.
London
| { | |
| "@context": "http://schema.org", | |
| "@type": ["Review", "ClaimReview"], | |
| "datePublished": "2014-07-23", | |
| "url": "http://www.politifact.com/texas/statements/2014/jul/23/rick-perry/rick-perry-claim-about-3000-homicides-illegal-immi/", | |
| "author": { | |
| "@type": "Organization", | |
| "url": "http://www.politifact.com/" | |
| "twitter": "@politifact" | |
| }, |
| If you'r using MAC Intel CPU you want to use VMware to virtual your desired OS such Windows or Ubuntu on the MAC OS you need to download VMware Fusion Player first then it has two version Pro and Player, the Player version is free for personal use but you need to create VM account to download and licence key. | |
| You can create account to download yourself here: | |
| https://customerconnect.vmware.com/group/vmware/evalcenter?p=fusion-player-personal | |
| If you don't want to create account to get license, you can try below original license key for VMware Fusion Player: | |
| COMPONENT: | |
| VMware Fusion Player – Personal Use |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| { | |
| "@context": "http://schema.org", | |
| "@type": ["Review", "ClaimReview"], | |
| "datePublished": "2014-07-23", | |
| "url": "http://www.politifact.com/texas/statements/2014/jul/23/rick-perry/rick-perry-claim-about-3000-homicides-illegal-immi/", | |
| "author": { | |
| "@type": "Organization", | |
| "url": "http://www.politifact.com/" | |
| "twitter": "@politifact" | |
| }, |
Tags: shioaji, sinotrade, 永豐, trading-api, query-only, python, ai-agent, openclaw
About: 用永豐 Shioaji 做「只查不下單」的個人帳戶查詢（庫存 / 餘額 / 損益 / 報價 / K 棒），並接成 AI agent 的 tool。這份只列設計重點、踩雷點與核心片段——scaffolding（venv / 安裝 / CLI 包裝）請自行對著官方文件補完。資訊量足以讓人或 agent 自己重建出完整專案。
| // LogiD configuration file snippet for Logitech MX Master 3S | |
| // Put this in /etc/logid.cfg (or merge into your existing file) | |
| // LogiD reads this to define per-device behavior and remaps. | |
| // "devices" is a list of device profiles LogiD should apply. | |
| devices: ( | |
| { | |
| // Match the device by its reported name. | |
| // Run `sudo logid -v` (or check logs) if you need to confirm the exact name. | |
| name: "MX Master 3S"; |